1 R Session Information

## R version 4.1.1 (2021-08-10)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Big Sur 10.16
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] bayestestR_0.13.1  rstanarm_2.21.4    Rcpp_1.0.10        ggprism_1.0.4     
##  [5] interactions_1.1.5 afex_1.3-0         lmerTest_3.1-3     lme4_1.1-33       
##  [9] Matrix_1.3-4       sjPlot_2.8.14      lubridate_1.9.2    forcats_1.0.0     
## [13] stringr_1.5.0      dplyr_1.1.2        purrr_1.0.1        readr_2.1.4       
## [17] tidyr_1.3.0        tibble_3.2.1       ggplot2_3.4.2      tidyverse_2.0.0   
## [21] plyr_1.8.8        
## 
## loaded via a namespace (and not attached):
##   [1] backports_1.4.1      jtools_2.2.1         igraph_1.4.2        
##   [4] splines_4.1.1        crosstalk_1.2.0      TH.data_1.1-2       
##   [7] rstantools_2.3.1     inline_0.3.19        digest_0.6.31       
##  [10] htmltools_0.5.5      fansi_1.0.4          magrittr_2.0.3      
##  [13] tzdb_0.3.0           modelr_0.1.11        RcppParallel_5.1.7  
##  [16] matrixStats_0.63.0   vroom_1.6.3          xts_0.13.1          
##  [19] sandwich_3.0-2       timechange_0.2.0     prettyunits_1.1.1   
##  [22] colorspace_2.1-0     xfun_0.39            callr_3.7.3         
##  [25] crayon_1.5.2         jsonlite_1.8.4       survival_3.5-5      
##  [28] zoo_1.8-12           glue_1.6.2           gtable_0.3.3        
##  [31] emmeans_1.8.5        sjstats_0.18.2       sjmisc_2.8.9        
##  [34] car_3.1-2            pkgbuild_1.4.0       rstan_2.21.8        
##  [37] abind_1.4-5          scales_1.2.1         mvtnorm_1.1-3       
##  [40] DBI_1.1.3            ggeffects_1.2.2      miniUI_0.1.1.1      
##  [43] xtable_1.8-4         performance_0.10.4   bit_4.0.5           
##  [46] stats4_4.1.1         StanHeaders_2.21.0-7 DT_0.28             
##  [49] htmlwidgets_1.6.2    threejs_0.3.3        ellipsis_0.3.2      
##  [52] pkgconfig_2.0.3      loo_2.6.0            sass_0.4.5          
##  [55] utf8_1.2.3           tidyselect_1.2.0     rlang_1.1.1         
##  [58] reshape2_1.4.4       later_1.3.1          munsell_0.5.0       
##  [61] tools_4.1.1          cachem_1.0.8         cli_3.6.1           
##  [64] generics_0.1.3       sjlabelled_1.2.0     broom_1.0.4         
##  [67] evaluate_0.20        fastmap_1.1.1        yaml_2.3.7          
##  [70] processx_3.8.1       knitr_1.42           bit64_4.0.5         
##  [73] pander_0.6.5         nlme_3.1-162         mime_0.12           
##  [76] compiler_4.1.1       bayesplot_1.10.0     shinythemes_1.2.0   
##  [79] rstudioapi_0.14      bslib_0.4.2          stringi_1.7.12      
##  [82] ps_1.7.5             lattice_0.21-8       nloptr_2.0.3        
##  [85] markdown_1.6         shinyjs_2.1.0        vctrs_0.6.2         
##  [88] pillar_1.9.0         lifecycle_1.0.3      jquerylib_0.1.4     
##  [91] estimability_1.4.1   insight_0.19.2       httpuv_1.6.11       
##  [94] R6_2.5.1             promises_1.2.0.1     gridExtra_2.3       
##  [97] codetools_0.2-19     boot_1.3-28.1        colourpicker_1.2.0  
## [100] MASS_7.3-60          gtools_3.9.4         withr_2.5.0         
## [103] shinystan_2.6.0      multcomp_1.4-23      parallel_4.1.1      
## [106] hms_1.1.3            grid_4.1.1           coda_0.19-4         
## [109] minqa_1.2.5          rmarkdown_2.21       carData_3.0-5       
## [112] numDeriv_2016.8-1.1  shiny_1.7.4          base64enc_0.1-3     
## [115] dygraphs_1.1.1.6

2 The effect of socialness on reaction times (log transformed)

2.1 LMM Results

Iterative procedure suggests modelling a random intercept per participant. Model with optimal random effects structure: logRT ~ Socialness*Concreteness + (1|participant) + (1|Word)

  logRT
Predictors Estimates CI Statistic p df
(Intercept) -0.03 -0.05 – -0.01 -2.62 0.011 83.34
Socialness1 -0.01 -0.02 – 0.00 -1.33 0.185 123.78
Concreteness 0.00 -0.00 – 0.01 0.63 0.533 124.78
Socialness1 ×
Concreteness
-0.00 -0.02 – 0.01 -0.68 0.496 124.76
Random Effects
σ2 0.01
τ00 Word 0.00
τ00 participant 0.01
ICC 0.42
Marginal R2 / Conditional R2 0.001 / 0.418

2.1.1 Check Assumptions

2.2 Bayesian ROPE analysis

The Bayesian analyses estimated that 82.91% of the HDI for socialness (90.4% PD), 100% for concreteness (72.99% PD), and 96.87% for the interaction (75.1% PD), fell within the ROPE (-0.015-0.015).

3 The effect of socialness on accuracy

3.1 LMM

Iterative procedure suggests modelling a random intercept per participant. Model with optimal random effects structure: Accuracy ~ Socialness*Concreteness + (1|participant) + (1|Word)

  ACC
Predictors Odds Ratios CI Statistic p
(Intercept) 32.42 20.78 – 50.59 15.32 <0.001
Socialness1 1.13 0.78 – 1.64 0.65 0.516
Concreteness 0.94 0.78 – 1.13 -0.70 0.483
Socialness1 ×
Concreteness
1.06 0.73 – 1.53 0.31 0.756
Random Effects
σ2 3.29
τ00 Word 0.92
τ00 participant 2.80
ICC 0.53
Marginal R2 / Conditional R2 0.001 / 0.531

## $Socialness

## 
## $Concreteness

3.1.1 Check Assumptions

## $Word

## 
## $participant

3.2 ROPE Analysis

The Bayesian analyses estimated that 57.96% of the HDI for socialness (73.86% PD), 90.39% for concreteness (75.37% PD), and 66.15% for the interaction (60.92% PD), fell within the ROPE (-0.181-0.181).

4 The effect of socialness on raw RTs

4.1 LMM

Iterative procedure suggests modelling a random slope per participant. Model with optimal random effects structure: RT ~ Socialness*Concreteness + (1|participant) + (1|Word)

  RT
Predictors Estimates CI p
(Intercept) 1.00 0.94 – 1.06 <0.001
Socialness1 -0.02 -0.77 – 0.72 0.457
Concreteness 0.00 -0.01 – 0.02 0.613
Socialness1 ×
Concreteness
-0.01 -0.05 – 0.02 0.475
Random Effects
σ2 0.11
τ00 Word 0.01
τ00 participant 0.06
ICC 0.37
N participant 73
N Word 132
Observations 8621
Marginal R2 / Conditional R2 0.001 / 0.373

## $Socialness

## 
## $Concreteness

4.1.1 Check Assumptions

4.2 ROPE Analysis